Overview

Dataset statistics

Number of variables22
Number of observations25252
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.2 MiB
Average record size in memory176.0 B

Variable types

Numeric16
Categorical6

Alerts

in_transit_qty is highly correlated with forecast_6_month and 6 other fieldsHigh correlation
forecast_3_month is highly correlated with forecast_6_month and 5 other fieldsHigh correlation
forecast_6_month is highly correlated with in_transit_qty and 6 other fieldsHigh correlation
forecast_9_month is highly correlated with in_transit_qty and 6 other fieldsHigh correlation
sales_1_month is highly correlated with in_transit_qty and 7 other fieldsHigh correlation
sales_3_month is highly correlated with in_transit_qty and 7 other fieldsHigh correlation
sales_6_month is highly correlated with in_transit_qty and 7 other fieldsHigh correlation
sales_9_month is highly correlated with in_transit_qty and 7 other fieldsHigh correlation
min_bank is highly correlated with in_transit_qty and 4 other fieldsHigh correlation
perf_6_month_avg is highly correlated with perf_12_month_avgHigh correlation
perf_12_month_avg is highly correlated with perf_6_month_avgHigh correlation
in_transit_qty is highly correlated with forecast_3_month and 7 other fieldsHigh correlation
forecast_3_month is highly correlated with in_transit_qty and 7 other fieldsHigh correlation
forecast_6_month is highly correlated with in_transit_qty and 7 other fieldsHigh correlation
forecast_9_month is highly correlated with in_transit_qty and 7 other fieldsHigh correlation
sales_1_month is highly correlated with in_transit_qty and 7 other fieldsHigh correlation
sales_3_month is highly correlated with in_transit_qty and 7 other fieldsHigh correlation
sales_6_month is highly correlated with in_transit_qty and 7 other fieldsHigh correlation
sales_9_month is highly correlated with in_transit_qty and 7 other fieldsHigh correlation
min_bank is highly correlated with in_transit_qty and 7 other fieldsHigh correlation
perf_6_month_avg is highly correlated with perf_12_month_avgHigh correlation
perf_12_month_avg is highly correlated with perf_6_month_avgHigh correlation
forecast_3_month is highly correlated with forecast_6_month and 3 other fieldsHigh correlation
forecast_6_month is highly correlated with forecast_3_month and 5 other fieldsHigh correlation
forecast_9_month is highly correlated with forecast_3_month and 5 other fieldsHigh correlation
sales_1_month is highly correlated with forecast_3_month and 6 other fieldsHigh correlation
sales_3_month is highly correlated with forecast_3_month and 6 other fieldsHigh correlation
sales_6_month is highly correlated with forecast_6_month and 5 other fieldsHigh correlation
sales_9_month is highly correlated with forecast_6_month and 5 other fieldsHigh correlation
min_bank is highly correlated with sales_1_month and 3 other fieldsHigh correlation
perf_6_month_avg is highly correlated with perf_12_month_avgHigh correlation
perf_12_month_avg is highly correlated with perf_6_month_avgHigh correlation
lead_time is highly correlated with perf_6_month_avg and 1 other fieldsHigh correlation
in_transit_qty is highly correlated with forecast_3_month and 7 other fieldsHigh correlation
forecast_3_month is highly correlated with in_transit_qty and 7 other fieldsHigh correlation
forecast_6_month is highly correlated with in_transit_qty and 7 other fieldsHigh correlation
forecast_9_month is highly correlated with in_transit_qty and 7 other fieldsHigh correlation
sales_1_month is highly correlated with in_transit_qty and 7 other fieldsHigh correlation
sales_3_month is highly correlated with in_transit_qty and 7 other fieldsHigh correlation
sales_6_month is highly correlated with in_transit_qty and 7 other fieldsHigh correlation
sales_9_month is highly correlated with in_transit_qty and 7 other fieldsHigh correlation
min_bank is highly correlated with in_transit_qty and 7 other fieldsHigh correlation
perf_6_month_avg is highly correlated with lead_time and 1 other fieldsHigh correlation
perf_12_month_avg is highly correlated with lead_time and 1 other fieldsHigh correlation
national_inv is highly skewed (γ1 = 21.55281716) Skewed
pieces_past_due is highly skewed (γ1 = 29.84564693) Skewed
sku has unique values Unique
national_inv has 2555 (10.1%) zeros Zeros
in_transit_qty has 20266 (80.3%) zeros Zeros
forecast_3_month has 14959 (59.2%) zeros Zeros
forecast_6_month has 13521 (53.5%) zeros Zeros
forecast_9_month has 12806 (50.7%) zeros Zeros
sales_1_month has 13220 (52.4%) zeros Zeros
sales_3_month has 10224 (40.5%) zeros Zeros
sales_6_month has 8629 (34.2%) zeros Zeros
sales_9_month has 7836 (31.0%) zeros Zeros
min_bank has 13123 (52.0%) zeros Zeros
pieces_past_due has 24582 (97.3%) zeros Zeros
perf_6_month_avg has 671 (2.7%) zeros Zeros
perf_12_month_avg has 547 (2.2%) zeros Zeros
local_bo_qty has 24510 (97.1%) zeros Zeros

Reproduction

Analysis started2021-10-22 17:21:02.856254
Analysis finished2021-10-22 17:21:47.144382
Duration44.29 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

sku
Real number (ℝ≥0)

UNIQUE

Distinct25252
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42161.59409
Minimum1
Maximum84170
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size197.4 KiB
2021-10-23T01:21:47.233146image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4271.75
Q120988.5
median42372
Q363313.25
95-th percentile79838.35
Maximum84170
Range84169
Interquartile range (IQR)42324.75

Descriptive statistics

Standard deviation24291.90894
Coefficient of variation (CV)0.5761620135
Kurtosis-1.206304855
Mean42161.59409
Median Absolute Deviation (MAD)21140
Skewness-0.009788689894
Sum1064664574
Variance590096840.1
MonotonicityStrictly increasing
2021-10-23T01:21:47.370777image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20471
 
< 0.1%
609511
 
< 0.1%
363791
 
< 0.1%
425261
 
< 0.1%
732571
 
< 0.1%
138681
 
< 0.1%
118231
 
< 0.1%
568811
 
< 0.1%
527871
 
< 0.1%
112431
 
< 0.1%
Other values (25242)25242
> 99.9%
ValueCountFrequency (%)
11
< 0.1%
31
< 0.1%
41
< 0.1%
61
< 0.1%
81
< 0.1%
121
< 0.1%
131
< 0.1%
141
< 0.1%
151
< 0.1%
171
< 0.1%
ValueCountFrequency (%)
841701
< 0.1%
841681
< 0.1%
841551
< 0.1%
841491
< 0.1%
841481
< 0.1%
841441
< 0.1%
841391
< 0.1%
841371
< 0.1%
841351
< 0.1%
841321
< 0.1%

national_inv
Real number (ℝ)

SKEWED
ZEROS

Distinct1616
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean213.4758435
Minimum-1238
Maximum73138
Zeros2555
Zeros (%)10.1%
Negative305
Negative (%)1.2%
Memory size197.4 KiB
2021-10-23T01:21:47.514394image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1238
5-th percentile0
Q13
median10
Q355
95-th percentile592
Maximum73138
Range74376
Interquartile range (IQR)52

Descriptive statistics

Standard deviation1632.760586
Coefficient of variation (CV)7.648455952
Kurtosis621.4059689
Mean213.4758435
Median Absolute Deviation (MAD)9
Skewness21.55281716
Sum5390692
Variance2665907.131
MonotonicityNot monotonic
2021-10-23T01:21:47.662748image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02555
 
10.1%
21781
 
7.1%
31478
 
5.9%
11162
 
4.6%
41127
 
4.5%
5967
 
3.8%
6813
 
3.2%
7700
 
2.8%
10666
 
2.6%
8564
 
2.2%
Other values (1606)13439
53.2%
ValueCountFrequency (%)
-12381
 
< 0.1%
-1341
 
< 0.1%
-1301
 
< 0.1%
-1003
< 0.1%
-981
 
< 0.1%
-951
 
< 0.1%
-871
 
< 0.1%
-851
 
< 0.1%
-821
 
< 0.1%
-751
 
< 0.1%
ValueCountFrequency (%)
731381
< 0.1%
695001
< 0.1%
602251
< 0.1%
461701
< 0.1%
458671
< 0.1%
424051
< 0.1%
417851
< 0.1%
416121
< 0.1%
412181
< 0.1%
395921
< 0.1%

lead_time
Real number (ℝ≥0)

HIGH CORRELATION

Distinct22
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.89806748
Minimum0
Maximum28
Zeros183
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size197.4 KiB
2021-10-23T01:21:47.790407image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median8
Q38
95-th percentile12
Maximum28
Range28
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.520616682
Coefficient of variation (CV)0.5103772458
Kurtosis-0.6154051589
Mean6.89806748
Median Absolute Deviation (MAD)1
Skewness-0.03550105442
Sum174190
Variance12.39474182
MonotonicityNot monotonic
2021-10-23T01:21:47.890141image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
810848
43.0%
25853
23.2%
123218
 
12.7%
42013
 
8.0%
91925
 
7.6%
3300
 
1.2%
10216
 
0.9%
0183
 
0.7%
14151
 
0.6%
16129
 
0.5%
Other values (12)416
 
1.6%
ValueCountFrequency (%)
0183
 
0.7%
25853
23.2%
3300
 
1.2%
42013
 
8.0%
577
 
0.3%
6103
 
0.4%
75
 
< 0.1%
810848
43.0%
91925
 
7.6%
10216
 
0.9%
ValueCountFrequency (%)
281
 
< 0.1%
251
 
< 0.1%
223
 
< 0.1%
2010
 
< 0.1%
182
 
< 0.1%
1753
 
0.2%
16129
0.5%
1568
0.3%
14151
0.6%
1366
0.3%

in_transit_qty
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct555
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.03884841
Minimum0
Maximum3726
Zeros20266
Zeros (%)80.3%
Negative0
Negative (%)0.0%
Memory size197.4 KiB
2021-10-23T01:21:48.014841image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile57
Maximum3726
Range3726
Interquartile range (IQR)0

Descriptive statistics

Standard deviation126.4874591
Coefficient of variation (CV)7.011947559
Kurtosis312.9411674
Mean18.03884841
Median Absolute Deviation (MAD)0
Skewness15.36020216
Sum455517
Variance15999.0773
MonotonicityNot monotonic
2021-10-23T01:21:48.150446image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
020266
80.3%
1583
 
2.3%
2364
 
1.4%
3265
 
1.0%
4228
 
0.9%
6182
 
0.7%
5176
 
0.7%
8133
 
0.5%
10116
 
0.5%
12112
 
0.4%
Other values (545)2827
 
11.2%
ValueCountFrequency (%)
020266
80.3%
1583
 
2.3%
2364
 
1.4%
3265
 
1.0%
4228
 
0.9%
5176
 
0.7%
6182
 
0.7%
7106
 
0.4%
8133
 
0.5%
990
 
0.4%
ValueCountFrequency (%)
37261
< 0.1%
37221
< 0.1%
36441
< 0.1%
35501
< 0.1%
35001
< 0.1%
33601
< 0.1%
31281
< 0.1%
30801
< 0.1%
30721
< 0.1%
29201
< 0.1%

forecast_3_month
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1008
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89.19855853
Minimum0
Maximum16300
Zeros14959
Zeros (%)59.2%
Negative0
Negative (%)0.0%
Memory size197.4 KiB
2021-10-23T01:21:48.437701image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312
95-th percentile360
Maximum16300
Range16300
Interquartile range (IQR)12

Descriptive statistics

Standard deviation515.0172037
Coefficient of variation (CV)5.773828772
Kurtosis300.4407618
Mean89.19855853
Median Absolute Deviation (MAD)0
Skewness14.611488
Sum2252442
Variance265242.7201
MonotonicityNot monotonic
2021-10-23T01:21:48.573338image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
014959
59.2%
1601
 
2.4%
2549
 
2.2%
5436
 
1.7%
4409
 
1.6%
3373
 
1.5%
10325
 
1.3%
6293
 
1.2%
12259
 
1.0%
8225
 
0.9%
Other values (998)6823
27.0%
ValueCountFrequency (%)
014959
59.2%
1601
 
2.4%
2549
 
2.2%
3373
 
1.5%
4409
 
1.6%
5436
 
1.7%
6293
 
1.2%
7206
 
0.8%
8225
 
0.9%
9153
 
0.6%
ValueCountFrequency (%)
163001
< 0.1%
154561
< 0.1%
153431
< 0.1%
150001
< 0.1%
144001
< 0.1%
139021
< 0.1%
130151
< 0.1%
106591
< 0.1%
106481
< 0.1%
105001
< 0.1%

forecast_6_month
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1344
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean164.663789
Minimum0
Maximum28000
Zeros13521
Zeros (%)53.5%
Negative0
Negative (%)0.0%
Memory size197.4 KiB
2021-10-23T01:21:48.727186image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q326
95-th percentile636.9
Maximum28000
Range28000
Interquartile range (IQR)26

Descriptive statistics

Standard deviation941.8269453
Coefficient of variation (CV)5.719696789
Kurtosis293.4173668
Mean164.663789
Median Absolute Deviation (MAD)0
Skewness14.52443836
Sum4158090
Variance887037.9949
MonotonicityNot monotonic
2021-10-23T01:21:48.862824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
013521
53.5%
1514
 
2.0%
2475
 
1.9%
3437
 
1.7%
4382
 
1.5%
5360
 
1.4%
6348
 
1.4%
10314
 
1.2%
8275
 
1.1%
7270
 
1.1%
Other values (1334)8356
33.1%
ValueCountFrequency (%)
013521
53.5%
1514
 
2.0%
2475
 
1.9%
3437
 
1.7%
4382
 
1.5%
5360
 
1.4%
6348
 
1.4%
7270
 
1.1%
8275
 
1.1%
9157
 
0.6%
ValueCountFrequency (%)
280001
< 0.1%
270481
< 0.1%
268801
< 0.1%
264001
< 0.1%
260681
< 0.1%
257401
< 0.1%
250571
< 0.1%
233201
< 0.1%
210001
< 0.1%
200301
< 0.1%

forecast_9_month
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1604
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean243.2605734
Minimum0
Maximum42000
Zeros12806
Zeros (%)50.7%
Negative0
Negative (%)0.0%
Memory size197.4 KiB
2021-10-23T01:21:49.009968image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q340
95-th percentile941.45
Maximum42000
Range42000
Interquartile range (IQR)40

Descriptive statistics

Standard deviation1411.522973
Coefficient of variation (CV)5.802514371
Kurtosis310.8687612
Mean243.2605734
Median Absolute Deviation (MAD)0
Skewness14.9954653
Sum6142816
Variance1992397.104
MonotonicityNot monotonic
2021-10-23T01:21:49.137145image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
012806
50.7%
1448
 
1.8%
2444
 
1.8%
3405
 
1.6%
5381
 
1.5%
4362
 
1.4%
6337
 
1.3%
10331
 
1.3%
8296
 
1.2%
12259
 
1.0%
Other values (1594)9183
36.4%
ValueCountFrequency (%)
012806
50.7%
1448
 
1.8%
2444
 
1.8%
3405
 
1.6%
4362
 
1.4%
5381
 
1.5%
6337
 
1.3%
7216
 
0.9%
8296
 
1.2%
9185
 
0.7%
ValueCountFrequency (%)
420001
< 0.1%
413401
< 0.1%
407821
< 0.1%
400201
< 0.1%
384001
< 0.1%
372961
< 0.1%
367241
< 0.1%
362801
< 0.1%
353831
< 0.1%
327751
< 0.1%

sales_1_month
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct679
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.53136385
Minimum0
Maximum4907
Zeros13220
Zeros (%)52.4%
Negative0
Negative (%)0.0%
Memory size197.4 KiB
2021-10-23T01:21:49.273801image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36
95-th percentile102
Maximum4907
Range4907
Interquartile range (IQR)6

Descriptive statistics

Standard deviation142.1103457
Coefficient of variation (CV)5.356315135
Kurtosis245.4609087
Mean26.53136385
Median Absolute Deviation (MAD)0
Skewness13.15885423
Sum669970
Variance20195.35037
MonotonicityNot monotonic
2021-10-23T01:21:49.419381image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
013220
52.4%
12361
 
9.3%
21331
 
5.3%
3787
 
3.1%
4684
 
2.7%
5540
 
2.1%
6444
 
1.8%
7356
 
1.4%
8346
 
1.4%
9275
 
1.1%
Other values (669)4908
 
19.4%
ValueCountFrequency (%)
013220
52.4%
12361
 
9.3%
21331
 
5.3%
3787
 
3.1%
4684
 
2.7%
5540
 
2.1%
6444
 
1.8%
7356
 
1.4%
8346
 
1.4%
9275
 
1.1%
ValueCountFrequency (%)
49071
< 0.1%
39711
< 0.1%
38011
< 0.1%
34801
< 0.1%
31081
< 0.1%
30771
< 0.1%
29661
< 0.1%
29601
< 0.1%
29001
< 0.1%
28141
< 0.1%

sales_3_month
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1186
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.71748772
Minimum0
Maximum14464
Zeros10224
Zeros (%)40.5%
Negative0
Negative (%)0.0%
Memory size197.4 KiB
2021-10-23T01:21:49.571973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q318
95-th percentile332.9
Maximum14464
Range14464
Interquartile range (IQR)18

Descriptive statistics

Standard deviation438.4914875
Coefficient of variation (CV)5.237752582
Kurtosis242.0126248
Mean83.71748772
Median Absolute Deviation (MAD)2
Skewness13.03895944
Sum2114034
Variance192274.7846
MonotonicityNot monotonic
2021-10-23T01:21:49.715294image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
010224
40.5%
11987
 
7.9%
21268
 
5.0%
3906
 
3.6%
4666
 
2.6%
5552
 
2.2%
6478
 
1.9%
7395
 
1.6%
8337
 
1.3%
10311
 
1.2%
Other values (1176)8128
32.2%
ValueCountFrequency (%)
010224
40.5%
11987
 
7.9%
21268
 
5.0%
3906
 
3.6%
4666
 
2.6%
5552
 
2.2%
6478
 
1.9%
7395
 
1.6%
8337
 
1.3%
9308
 
1.2%
ValueCountFrequency (%)
144641
< 0.1%
119981
< 0.1%
112801
< 0.1%
112771
< 0.1%
102421
< 0.1%
102111
< 0.1%
99921
< 0.1%
97311
< 0.1%
94701
< 0.1%
89301
< 0.1%

sales_6_month
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1627
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean165.1147236
Minimum0
Maximum26512
Zeros8629
Zeros (%)34.2%
Negative0
Negative (%)0.0%
Memory size197.4 KiB
2021-10-23T01:21:49.864893image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q336
95-th percentile644.45
Maximum26512
Range26512
Interquartile range (IQR)36

Descriptive statistics

Standard deviation874.1210956
Coefficient of variation (CV)5.294022705
Kurtosis248.0567378
Mean165.1147236
Median Absolute Deviation (MAD)4
Skewness13.28757677
Sum4169477
Variance764087.6899
MonotonicityNot monotonic
2021-10-23T01:21:50.022472image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
08629
34.2%
11827
 
7.2%
21198
 
4.7%
3807
 
3.2%
4686
 
2.7%
5493
 
2.0%
6469
 
1.9%
7378
 
1.5%
8344
 
1.4%
9316
 
1.3%
Other values (1617)10105
40.0%
ValueCountFrequency (%)
08629
34.2%
11827
 
7.2%
21198
 
4.7%
3807
 
3.2%
4686
 
2.7%
5493
 
2.0%
6469
 
1.9%
7378
 
1.5%
8344
 
1.4%
9316
 
1.3%
ValueCountFrequency (%)
265121
< 0.1%
256301
< 0.1%
229041
< 0.1%
223971
< 0.1%
223921
< 0.1%
221521
< 0.1%
205141
< 0.1%
189021
< 0.1%
186781
< 0.1%
178831
< 0.1%

sales_9_month
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1937
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean249.6094171
Minimum0
Maximum42410
Zeros7836
Zeros (%)31.0%
Negative0
Negative (%)0.0%
Memory size197.4 KiB
2021-10-23T01:21:50.171330image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6
Q352
95-th percentile970.45
Maximum42410
Range42410
Interquartile range (IQR)52

Descriptive statistics

Standard deviation1339.618948
Coefficient of variation (CV)5.36686061
Kurtosis261.88017
Mean249.6094171
Median Absolute Deviation (MAD)6
Skewness13.63280407
Sum6303137
Variance1794578.927
MonotonicityNot monotonic
2021-10-23T01:21:50.298988image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
07836
31.0%
11677
 
6.6%
21117
 
4.4%
3827
 
3.3%
4638
 
2.5%
5521
 
2.1%
6430
 
1.7%
8362
 
1.4%
7351
 
1.4%
9318
 
1.3%
Other values (1927)11175
44.3%
ValueCountFrequency (%)
07836
31.0%
11677
 
6.6%
21117
 
4.4%
3827
 
3.3%
4638
 
2.5%
5521
 
2.1%
6430
 
1.7%
7351
 
1.4%
8362
 
1.4%
9318
 
1.3%
ValueCountFrequency (%)
424101
< 0.1%
396911
< 0.1%
378721
< 0.1%
333331
< 0.1%
329781
< 0.1%
318331
< 0.1%
317231
< 0.1%
305961
< 0.1%
302981
< 0.1%
282581
< 0.1%

min_bank
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct644
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.62422778
Minimum0
Maximum3386
Zeros13123
Zeros (%)52.0%
Negative0
Negative (%)0.0%
Memory size197.4 KiB
2021-10-23T01:21:50.443603image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile115
Maximum3386
Range3386
Interquartile range (IQR)3

Descriptive statistics

Standard deviation134.0587735
Coefficient of variation (CV)5.035217344
Kurtosis199.9160793
Mean26.62422778
Median Absolute Deviation (MAD)0
Skewness12.23090191
Sum672315
Variance17971.75475
MonotonicityNot monotonic
2021-10-23T01:21:50.581259image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
013123
52.0%
13732
 
14.8%
21618
 
6.4%
3541
 
2.1%
4340
 
1.3%
5225
 
0.9%
6178
 
0.7%
7154
 
0.6%
8131
 
0.5%
13121
 
0.5%
Other values (634)5089
 
20.2%
ValueCountFrequency (%)
013123
52.0%
13732
 
14.8%
21618
 
6.4%
3541
 
2.1%
4340
 
1.3%
5225
 
0.9%
6178
 
0.7%
7154
 
0.6%
8131
 
0.5%
997
 
0.4%
ValueCountFrequency (%)
33861
< 0.1%
33611
< 0.1%
30941
< 0.1%
30451
< 0.1%
29861
< 0.1%
29361
< 0.1%
28901
< 0.1%
28591
< 0.1%
28451
< 0.1%
27771
< 0.1%

potential_issue
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size197.4 KiB
0
25232 
1
 
20

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
025232
99.9%
120
 
0.1%

Length

2021-10-23T01:21:50.849761image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-23T01:21:50.925558image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
025232
99.9%
120
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

pieces_past_due
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct111
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9297877396
Minimum0
Maximum720
Zeros24582
Zeros (%)97.3%
Negative0
Negative (%)0.0%
Memory size197.4 KiB
2021-10-23T01:21:51.024178image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum720
Range720
Interquartile range (IQR)0

Descriptive statistics

Standard deviation13.40581047
Coefficient of variation (CV)14.41814072
Kurtosis1154.485738
Mean0.9297877396
Median Absolute Deviation (MAD)0
Skewness29.84564693
Sum23479
Variance179.7157544
MonotonicityNot monotonic
2021-10-23T01:21:51.161811image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
024582
97.3%
192
 
0.4%
245
 
0.2%
337
 
0.1%
428
 
0.1%
524
 
0.1%
1024
 
0.1%
622
 
0.1%
1821
 
0.1%
1219
 
0.1%
Other values (101)358
 
1.4%
ValueCountFrequency (%)
024582
97.3%
192
 
0.4%
245
 
0.2%
337
 
0.1%
428
 
0.1%
524
 
0.1%
622
 
0.1%
716
 
0.1%
812
 
< 0.1%
912
 
< 0.1%
ValueCountFrequency (%)
7201
 
< 0.1%
6003
< 0.1%
5401
 
< 0.1%
5141
 
< 0.1%
4001
 
< 0.1%
3501
 
< 0.1%
3361
 
< 0.1%
3261
 
< 0.1%
3251
 
< 0.1%
3161
 
< 0.1%

perf_6_month_avg
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct101
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7715622525
Minimum0
Maximum1
Zeros671
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size197.4 KiB
2021-10-23T01:21:51.309419image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.19
Q10.69
median0.83
Q30.97
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.28

Descriptive statistics

Standard deviation0.2446894816
Coefficient of variation (CV)0.3171351124
Kurtosis1.858815432
Mean0.7715622525
Median Absolute Deviation (MAD)0.14
Skewness-1.500968051
Sum19483.49
Variance0.05987294242
MonotonicityNot monotonic
2021-10-23T01:21:51.452141image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.992254
 
8.9%
11991
 
7.9%
0.731750
 
6.9%
0.981340
 
5.3%
0.97994
 
3.9%
0.78712
 
2.8%
0.95690
 
2.7%
0671
 
2.7%
0.82665
 
2.6%
0.96646
 
2.6%
Other values (91)13539
53.6%
ValueCountFrequency (%)
0671
2.7%
0.0111
 
< 0.1%
0.0218
 
0.1%
0.0311
 
< 0.1%
0.0417
 
0.1%
0.0536
 
0.1%
0.0626
 
0.1%
0.0741
 
0.2%
0.0833
 
0.1%
0.0934
 
0.1%
ValueCountFrequency (%)
11991
7.9%
0.992254
8.9%
0.981340
5.3%
0.97994
3.9%
0.96646
 
2.6%
0.95690
 
2.7%
0.94567
 
2.2%
0.93535
 
2.1%
0.92329
 
1.3%
0.91477
 
1.9%

perf_12_month_avg
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct101
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.768751386
Minimum0
Maximum1
Zeros547
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size197.4 KiB
2021-10-23T01:21:51.604246image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.23
Q10.68
median0.82
Q30.95
95-th percentile0.99
Maximum1
Range1
Interquartile range (IQR)0.27

Descriptive statistics

Standard deviation0.2354178177
Coefficient of variation (CV)0.3062340075
Kurtosis2.008194421
Mean0.768751386
Median Absolute Deviation (MAD)0.14
Skewness-1.526624409
Sum19412.51
Variance0.0554215489
MonotonicityNot monotonic
2021-10-23T01:21:51.770321image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.992101
 
8.3%
0.781818
 
7.2%
0.981464
 
5.8%
0.971095
 
4.3%
0.96986
 
3.9%
0.66936
 
3.7%
0.95737
 
2.9%
0.9729
 
2.9%
0.82656
 
2.6%
0.79635
 
2.5%
Other values (91)14095
55.8%
ValueCountFrequency (%)
0547
2.2%
0.0128
 
0.1%
0.0213
 
0.1%
0.039
 
< 0.1%
0.0420
 
0.1%
0.0516
 
0.1%
0.0611
 
< 0.1%
0.0719
 
0.1%
0.0817
 
0.1%
0.0947
 
0.2%
ValueCountFrequency (%)
1621
 
2.5%
0.992101
8.3%
0.981464
5.8%
0.971095
4.3%
0.96986
3.9%
0.95737
 
2.9%
0.94616
 
2.4%
0.93545
 
2.2%
0.92466
 
1.8%
0.91410
 
1.6%

local_bo_qty
Real number (ℝ≥0)

ZEROS

Distinct80
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3449231744
Minimum0
Maximum131
Zeros24510
Zeros (%)97.1%
Negative0
Negative (%)0.0%
Memory size197.4 KiB
2021-10-23T01:21:51.923937image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum131
Range131
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.936064409
Coefficient of variation (CV)11.41142347
Kurtosis384.7562865
Mean0.3449231744
Median Absolute Deviation (MAD)0
Skewness17.94419664
Sum8710
Variance15.49260303
MonotonicityNot monotonic
2021-10-23T01:21:52.077494image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
024510
97.1%
1214
 
0.8%
2107
 
0.4%
355
 
0.2%
442
 
0.2%
636
 
0.1%
525
 
0.1%
1022
 
0.1%
820
 
0.1%
719
 
0.1%
Other values (70)202
 
0.8%
ValueCountFrequency (%)
024510
97.1%
1214
 
0.8%
2107
 
0.4%
355
 
0.2%
442
 
0.2%
525
 
0.1%
636
 
0.1%
719
 
0.1%
820
 
0.1%
910
 
< 0.1%
ValueCountFrequency (%)
1311
 
< 0.1%
1241
 
< 0.1%
1131
 
< 0.1%
1004
< 0.1%
991
 
< 0.1%
931
 
< 0.1%
921
 
< 0.1%
911
 
< 0.1%
901
 
< 0.1%
891
 
< 0.1%

deck_risk
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size197.4 KiB
0
20643 
1
4609 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
020643
81.7%
14609
 
18.3%

Length

2021-10-23T01:21:52.223105image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-23T01:21:52.293917image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
020643
81.7%
14609
 
18.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

oe_constraint
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size197.4 KiB
0
25243 
1
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
025243
> 99.9%
19
 
< 0.1%

Length

2021-10-23T01:21:52.386907image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-23T01:21:52.469666image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
025243
> 99.9%
19
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ppap_risk
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size197.4 KiB
0
22135 
1
3117 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
022135
87.7%
13117
 
12.3%

Length

2021-10-23T01:21:52.550451image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-23T01:21:52.623615image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
022135
87.7%
13117
 
12.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

stop_auto_buy
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size197.4 KiB
1
24798 
0
 
454

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
124798
98.2%
0454
 
1.8%

Length

2021-10-23T01:21:52.716225image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-23T01:21:52.789054image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
124798
98.2%
0454
 
1.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

rev_stop
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size197.4 KiB
0
25246 
1
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
025246
> 99.9%
16
 
< 0.1%

Length

2021-10-23T01:21:52.870346image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-23T01:21:52.947358image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
025246
> 99.9%
16
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2021-10-23T01:21:43.689662image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-10-23T01:21:10.285439image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-10-23T01:21:31.190621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-23T01:21:33.388097image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-23T01:21:35.865917image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-23T01:21:38.313652image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-23T01:21:40.801493image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-23T01:21:43.114239image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-23T01:21:45.712039image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-23T01:21:09.859341image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-23T01:21:12.216820image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-23T01:21:14.621669image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-23T01:21:16.995461image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-23T01:21:19.568762image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-23T01:21:21.873175image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-23T01:21:24.212797image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-23T01:21:26.489036image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-23T01:21:29.045019image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-23T01:21:31.332818image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-23T01:21:33.647402image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-23T01:21:36.017120image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-23T01:21:38.460261image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-23T01:21:40.948102image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-23T01:21:43.266921image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-23T01:21:45.852931image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-23T01:21:10.001960image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-23T01:21:12.369411image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-23T01:21:14.748354image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-23T01:21:17.142601image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-23T01:21:19.712508image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-23T01:21:22.009777image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-23T01:21:24.344445image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-23T01:21:26.625695image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-23T01:21:29.193943image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-23T01:21:31.464520image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-23T01:21:33.784960image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-23T01:21:36.164726image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-23T01:21:38.613914image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-23T01:21:41.088352image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-23T01:21:43.410096image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-23T01:21:46.003333image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-23T01:21:10.146785image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-23T01:21:12.510728image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-23T01:21:14.887956image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-23T01:21:17.290175image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-23T01:21:19.864125image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-23T01:21:22.150973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-23T01:21:24.474098image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-23T01:21:26.761259image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-23T01:21:29.338731image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-23T01:21:31.601171image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-23T01:21:33.919601image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-23T01:21:36.316346image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-23T01:21:38.917669image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-23T01:21:41.233963image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-23T01:21:43.544270image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-10-23T01:21:53.050449image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-10-23T01:21:53.437988image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-10-23T01:21:53.815978image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-10-23T01:21:54.286298image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2021-10-23T01:21:54.489054image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-10-23T01:21:46.286718image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-10-23T01:21:46.889273image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

skunational_invlead_timein_transit_qtyforecast_3_monthforecast_6_monthforecast_9_monthsales_1_monthsales_3_monthsales_6_monthsales_9_monthmin_bankpotential_issuepieces_past_dueperf_6_month_avgperf_12_month_avglocal_bo_qtydeck_riskoe_constraintppap_riskstop_auto_buyrev_stop
01612000000000000.570.68000010
13028183042103058833000.930.94000010
2468000000000000.870.88010010
361014000001110000.000.10010010
481489800001420220000.900.94000010
51238000000001000.370.34000010
61312011172431019200000.990.99010010
71417084715629643653160294482129000.620.68000010
81598022032038044109189289421201.000.89200010
91718012202441012182000.970.97000010

Last rows

skunational_invlead_timein_transit_qtyforecast_3_monthforecast_6_monthforecast_9_monthsales_1_monthsales_3_monthsales_6_monthsales_9_monthmin_bankpotential_issuepieces_past_dueperf_6_month_avgperf_12_month_avglocal_bo_qtydeck_riskoe_constraintppap_riskstop_auto_buyrev_stop
252428413217580000938668325000.960.97000010
25243841351806101414890000.370.34000010
2524484137212035713460000.920.94000010
25245841393048443526829461183667471135114000.990.97000010
25246841441220515202519390000.980.99000010
25247841481280000149160000.830.86000010
25248841493221200802392018940164246000.780.78000010
252498415564000011130000.730.78010010
2525084168512011100000000.730.79000110
252518417042521080142419832864554650000.960.96000010